Abstract
AbstractA nonequilibrium open‐dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there is a challenge in the previously proposed approach: The machine can be trapped by local minima which increases exponentially with a problem size. This leads to erroneous solutions rather than correct answers. In this paper, it is shown that it is possible to overcome this problem partially by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low‐energy excited states via its inherent exploration property during a solution search process.
Highlights
Coherent Ising machines with error correction feedbackSatoshi Kako* Timothee Leleu Yoshitaka Inui Farad Khoyratee Sam Reifenstein Yoshihisa YamamotoKeywords: Coherent Ising machine, Nonlinear optics, Optical parametric oscillators, Combinatorial optimization, Artificial neural network, Amplitude squeezing, Random samplingA non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems
The quantum noise correlation formed among degenerate optical parametric oscillators (DOPOs) realizes a quantum parallel search to identify a ground state before sizable mean-fields build up in DOPOs, while the pitchfork bifurcation above threshold amplifies the amplitude of a selected ground state exponentially to form a deterministic computation result.[16, 17]
When a coherent Ising machines (CIMs) consists of DOPOs with a large saturation parameter, the success probability of finding ground states is greatly improved for hard instances
Summary
Satoshi Kako* Timothee Leleu Yoshitaka Inui Farad Khoyratee Sam Reifenstein Yoshihisa Yamamoto. A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. There is a challenge in the previously proposed approach: The machine can be trapped by local minima which increases exponentially with a problem size. This leads to erroneous solutions rather than correct answers. It is shown that it is possible to overcome this problem partially by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low-energy excited states via its inherent exploration property during a solution search process
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